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An improved quantum-behaved particle swarm optimization for multi-peak optimization problems

An improved quantum-behaved particle swarm optimization for multi-peak optimization problems

Zhao, Ji, Sun, Jun, Lai, Choi-Hong ORCID: 0000-0002-7558-6398 and Xu, Wenbo (2011) An improved quantum-behaved particle swarm optimization for multi-peak optimization problems. International Journal of Computer Mathematics, 88 (3). pp. 517-532. ISSN 0020-7160 (doi:https://doi.org/10.1080/00207160903521501)

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Abstract

In this paper, we propose an improved quantum-behaved particle swarm optimization (QPSO), namely species-based QPSO (SQPSO), using the notion of species for solving multi peak optimization problems. In the proposed SQPSO, the population is divided into subpopulations (species) based on their similarities. Each species is grouped around a dominating particle called the species seed. During the process of iterations, species are able to simultaneously optimize towards multiple optima by using QPSO, so each peak will definitely be searched in parallel, regardless of whether it is global or local optima. Further, SQPSO is applied to solve systems of nonlinear equations describing certain fitness functions, which are multi-peak functions. Our experiments demonstrate that SQPSO is able to search multiple peaks of a given function as accurate and efficient as possible. Finally the experiments for the solutions of systems of nonlinear equations show that the algorithm is successful in locating multiple solutions with better accuracy.

Item Type: Article
Uncontrolled Keywords: evolutionary computation, quantum-behaved particle swarm optimization, particle swarm optimization, species, multi-peak optimization problems, systems of nonlinear equations
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Related URLs:
Last Modified: 14 Oct 2016 09:19
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/7574

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